The main parameters and methods influencing the removal of Gentian Violet (GV) dye from aqueous media were investigated using a stachy plant in this study. The surface of the stachy plant was determined using FTIR spectra. Adsorption is influenced by the adsorbent's characteristic groups. The research took into account the usual conditions for GV dye adsorption by the stachy plant, such as the impact of contact time. Mass dosage , after 0.3 g the amount of adsorbed dye declines. Study pH and ionic strength, the results obtained showed that at pH 3 the largest adsorption of (GV) was seen, while at pH 9, the lowest adsorption was observed  at 298 K, the adsorption kinetics and equilibrium constants were achieved, and the equilibr

Measuring the efficiency of postgraduate and undergraduate programs is one of the essential elements in educational process. In this study, colleges of Baghdad University and data for the academic year (2011-2012) have been chosen to measure the relative efficiencies of postgraduate and undergraduate programs in terms of their inputs and outputs. A relevant method to conduct the analysis of this data is Data Envelopment Analysis (DEA). The effect of academic staff to the number of enrolled and alumni students to the postgraduate and undergraduate programs are the main focus of the study.

Background: Sprite coding is a very effective technique for clarifying the background video object. The sprite generation is an open issue because of the foreground objects which prevent the precision of camera motion estimation and blurs the created sprite. Objective: In this paper, a quick and basic static method for sprite area detection in video data is presented. Two statistical methods are applied; the mean and standard deviation of every pixel (over all group of video frame) to determine whether the pixel is a piece of the selected static sprite range or not. A binary map array is built for demonstrating the allocated sprite (as 1) while the non-sprite (as 0) pixels valued. Likewise, holes and gaps filling strategy was utilized to re

      Cyberbullying is one of the biggest electronic problems that takes multiple forms of harassment using various social media. Currently, this phenomenon has become very common and is increasing, especially for young people and adolescents. Negative comments have a significant and dangerous impact on society in general and on adolescents in particular. Therefore, one of the most successful prevention methods is to detect and block harmful messages and comments. In this research, negative Arabic comments that refer to cyberbullying will be detected using a support vector machine algorithm. The term frequency-inverse document frequency vectorizer and the count vectorizer methods were used for feature extraction, and the results wer

Leishmaniasis is a widespread parasitic disease that occurs as a result of infection with a unicellular parasite belonging to the genus Leishmania. Diagnosis by conventional methods is inaccurate and is not sensitive to confirm the genus infection. Here, we have investigated a methods for Leishmania genus diagnosis, which includes the technique of polymerase chain reaction to detect the presence of the parasite at in vitro for promastigote cultures using three genus-specific primer pairs to amplify HSP70, ITS, and ITS2. The results showed single band of ~1422, ~1020, and ~550 respectively. This study has proved the ability of these primer pairs to detect Leishmania infection and recommend them to be used for detection of leishmaniasis in

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential f